English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

In this work, we propose a comprehensive analysis of the genomic features of the human in mutations to classify loss-of-function (LoF) and gain-of-function (GoF) mutations. Through these genetic mutations, a protein can lose its native function, or it can confer a new function. However, when a mutation occurs, it is difficult to determine whether it will result in a LoF or a GoF. Therefore, we propose a study that analyzes the human genomic features of LoF and GoF instances to find features that can be used to classify LoF and GoF mutations. In order to collect experimentally verified LoF and GoF mutational information, we obtained 816 LoF mutations and 474 GoF mutations from a literature text-mining process. Next, with data-preprocessing steps, 258 LoF and 129 GoF mutations remained for a further analysis. We analyzed the properties of these LoF and GoF mutations. Among the properties, we selected features which show different tendencies between the two groups. Finally, we implemented classifications using support vector machine, random forest, and logistic regression methods to confirm whether or not these features can identify LoF and GoF mutations. By implementing classifications with the selected features, it is demonstrated that the selected features have good discriminative power.

Identification of Genomic Features in the Classification of Loss- and Gain-of-Function Mutation